{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a0c7e900",
   "metadata": {},
   "source": [
    "Causal News Corpus uses English sentences from Subtask 4 of the CASE Multilingual Protest News Detection.\n",
    "\n",
    "* Github: https://github.com/emerging-welfare/case-2022-multilingual-event\n",
    "* Paper: https://aclanthology.org/2021.case-1.11.pdf\n",
    "\n",
    "Specifically, Subtask 4 works on Event Extraction, i.e. to extract event entity spans such as triggers and event arguments, from text. \n",
    "\n",
    "In this notebook, we add the Event information for our data, to provide more information for researchers to investigate further.\n",
    "\n",
    "Note: We only do not release the Events for the test set because the Subtask 4 shared task is an on-going annually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7f5be154",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "40aca253",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3248\n",
      "['The', 'State', 'alleged', 'they', 'hacked', 'Sabata', 'Petros', 'Chale', ',', '39', ',', 'to', 'death', 'in', 'Marikana', 'West', ',', 'on', 'December', '8', ',', '2016', ',', 'allegedly', 'over', 'the', 'allocation', 'of', 'low', 'cost', '(', 'RDP', ')', 'houses', 'at', 'Marikana', 'West', 'Extension', '2', '.']\n",
      "['O', 'O', 'O', 'O', 'B-trigger', 'B-target', 'I-target', 'I-target', 'O', 'O', 'O', 'O', 'O', 'O', 'B-place', 'I-place', 'O', 'B-etime', 'I-etime', 'I-etime', 'I-etime', 'I-etime', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-place', 'I-place', 'B-fname', 'I-fname', 'O']\n",
      "[0, 325, 650, 975, 1300, 1625, 1950, 2275, 2600, 2925, 3248]\n"
     ]
    },
    {
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>set</th>\n",
       "      <th>text</th>\n",
       "      <th>tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_01.txt</td>\n",
       "      <td>[The, State, alleged, they, hacked, Sabata, Pe...</td>\n",
       "      <td>[O, O, O, O, B-trigger, B-target, I-target, I-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_01.txt</td>\n",
       "      <td>[Chale, was, allegedly, chased, by, a, group, ...</td>\n",
       "      <td>[B-target, O, O, B-trigger, O, O, B-participan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_01.txt</td>\n",
       "      <td>[His, attackers, allegedly, drank, his, blood, .]</td>\n",
       "      <td>[O, B-participant, O, B-trigger, I-trigger, I-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train_01.txt</td>\n",
       "      <td>[Tip, asked, Mathunjwa, why, he, had, not, ``,...</td>\n",
       "      <td>[O, O, O, O, O, O, O, O, O, O, O, O, O, B-trig...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train_01.txt</td>\n",
       "      <td>[``, It, the, protest, was, something, which, ...</td>\n",
       "      <td>[O, O, O, B-trigger, O, O, O, O, O, O, O, O, O]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3243</th>\n",
       "      <td>train_10.txt</td>\n",
       "      <td>[Police, spokesman, ,, Lieutenant, Colonel, Th...</td>\n",
       "      <td>[O, O, O, O, O, O, O, O, O, O, O, B-etime, O, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3244</th>\n",
       "      <td>train_10.txt</td>\n",
       "      <td>[National, Freedom, Party, spokesman, ,, Sabel...</td>\n",
       "      <td>[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3245</th>\n",
       "      <td>train_10.txt</td>\n",
       "      <td>[He, described, the, shooting, as, ``, evil, '...</td>\n",
       "      <td>[O, O, O, B-trigger, O, O, O, O, O]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3246</th>\n",
       "      <td>train_10.txt</td>\n",
       "      <td>[On, Sunday, ,, three, explosions, in, the, so...</td>\n",
       "      <td>[B-etime, I-etime, O, O, B-trigger, O, O, O, O...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3247</th>\n",
       "      <td>train_10.txt</td>\n",
       "      <td>[Uighur, groups, abroad, say, the, clashes, ar...</td>\n",
       "      <td>[B-participant, O, O, O, O, B-trigger, O, O, O...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3248 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               set                                               text  \\\n",
       "0     train_01.txt  [The, State, alleged, they, hacked, Sabata, Pe...   \n",
       "1     train_01.txt  [Chale, was, allegedly, chased, by, a, group, ...   \n",
       "2     train_01.txt  [His, attackers, allegedly, drank, his, blood, .]   \n",
       "3     train_01.txt  [Tip, asked, Mathunjwa, why, he, had, not, ``,...   \n",
       "4     train_01.txt  [``, It, the, protest, was, something, which, ...   \n",
       "...            ...                                                ...   \n",
       "3243  train_10.txt  [Police, spokesman, ,, Lieutenant, Colonel, Th...   \n",
       "3244  train_10.txt  [National, Freedom, Party, spokesman, ,, Sabel...   \n",
       "3245  train_10.txt  [He, described, the, shooting, as, ``, evil, '...   \n",
       "3246  train_10.txt  [On, Sunday, ,, three, explosions, in, the, so...   \n",
       "3247  train_10.txt  [Uighur, groups, abroad, say, the, clashes, ar...   \n",
       "\n",
       "                                                   tags  \n",
       "0     [O, O, O, O, B-trigger, B-target, I-target, I-...  \n",
       "1     [B-target, O, O, B-trigger, O, O, B-participan...  \n",
       "2     [O, B-participant, O, B-trigger, I-trigger, I-...  \n",
       "3     [O, O, O, O, O, O, O, O, O, O, O, O, O, B-trig...  \n",
       "4       [O, O, O, B-trigger, O, O, O, O, O, O, O, O, O]  \n",
       "...                                                 ...  \n",
       "3243  [O, O, O, O, O, O, O, O, O, O, O, B-etime, O, ...  \n",
       "3244  [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ...  \n",
       "3245                [O, O, O, B-trigger, O, O, O, O, O]  \n",
       "3246  [B-etime, I-etime, O, O, B-trigger, O, O, O, O...  \n",
       "3247  [B-participant, O, O, O, O, B-trigger, O, O, O...  \n",
       "\n",
       "[3248 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Extract infos into List of Sentences & Tags\n",
    "def get_slices(num_examples, num_sets=10):\n",
    "    multiples = round(num_examples/num_sets)\n",
    "    slice_list = [i*multiples for i in range(num_sets)]\n",
    "    slice_list.append(num_examples)\n",
    "    return slice_list\n",
    "    \n",
    "\n",
    "def get_original(txt_file, num_sets=10):\n",
    "    sentences = []\n",
    "    tags = []\n",
    "\n",
    "    with open(txt_file, encoding='utf-8-sig') as file:\n",
    "        for line in file:\n",
    "            if line.strip() == '':\n",
    "                # append\n",
    "                sentences.append(curr_words)\n",
    "                tags.append(curr_tags)\n",
    "                curr_words = []\n",
    "                curr_tags = []\n",
    "            else:\n",
    "                word, tag = line.strip().split('\\t')\n",
    "                if word == 'SAMPLE_START':                \n",
    "                    curr_words = []\n",
    "                    curr_tags = []\n",
    "                elif word == '[SEP]':\n",
    "                    # append\n",
    "                    sentences.append(curr_words)\n",
    "                    tags.append(curr_tags)\n",
    "                    curr_words = []\n",
    "                    curr_tags = []\n",
    "                else:\n",
    "                    word = word.replace(u'\\ufeff', '').replace(r'[^\\x00-\\x7f]', '')\n",
    "                    curr_words.append(word)\n",
    "                    curr_tags.append(tag)\n",
    "\n",
    "    sentences.append(curr_words)\n",
    "    tags.append(curr_tags)\n",
    "\n",
    "    print(len(sentences))\n",
    "    print(sentences[0])\n",
    "    print(tags[0])\n",
    "\n",
    "\n",
    "    ### Split into Sets\n",
    "    slice_list = get_slices(len(sentences), num_sets)\n",
    "    print(slice_list)\n",
    "\n",
    "    \n",
    "    ### Format into DataFrame\n",
    "    df = []\n",
    "    for set_ in range(num_sets):\n",
    "        start = slice_list[set_]\n",
    "        end = slice_list[set_+1]\n",
    "        for i in range(start,end):\n",
    "            df.append(['train_{:02}.txt'.format(set_+1), sentences[i], tags[i]])      \n",
    "    assert(len(df)==len(sentences))\n",
    "\n",
    "\n",
    "    df = pd.DataFrame(df, columns=['set','text','tags'])\n",
    "    return df\n",
    "\n",
    "\n",
    "# Get \"en-train.txt\" from CASE organizers\n",
    "df = get_original(txt_file = r\"D:\\61 Challenges\\2022_CASE_\\FLAT_Annotation\\data\\en-train.txt\", num_sets=10)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9994e166",
   "metadata": {},
   "outputs": [
    {
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       "      <th>3067</th>\n",
       "      <td>train_09_B_157_2377</td>\n",
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       "      <th>3068</th>\n",
       "      <td>train_09_B_158_2503</td>\n",
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       "      <th>3069</th>\n",
       "      <td>train_09_B_159_2947</td>\n",
       "      <td>Ancheri Baby was shot dead on November 13 , 19...</td>\n",
       "      <td>0</td>\n",
       "      <td>train_09_B</td>\n",
       "      <td>159</td>\n",
       "      <td>2947</td>\n",
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       "      <th>3070</th>\n",
       "      <td>train_09_B_160_479</td>\n",
       "      <td>Mullanchira Mathai was beaten to death on Janu...</td>\n",
       "      <td>1</td>\n",
       "      <td>train_09_B</td>\n",
       "      <td>160</td>\n",
       "      <td>479</td>\n",
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       "      <th>3071</th>\n",
       "      <td>train_09_B_161_769</td>\n",
       "      <td>Muttukad Nanappan was stabbed to death on June...</td>\n",
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       "      <td>train_09_B</td>\n",
       "      <td>161</td>\n",
       "      <td>769</td>\n",
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       "<p>3072 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    index                                               text  \\\n",
       "0         train_01_0_1437  The State alleged they hacked Sabata Petros Ch...   \n",
       "1         train_01_1_3212  Chale was allegedly chased by a group of about...   \n",
       "2         train_01_2_3319          His attackers allegedly drank his blood .   \n",
       "3         train_01_3_1008  Tip asked Mathunjwa why he had not `` explicit...   \n",
       "4         train_01_4_2655  `` It the protest was something which happened...   \n",
       "...                   ...                                                ...   \n",
       "3067  train_09_B_157_2377  The first was shot , the second beaten to deat...   \n",
       "3068  train_09_B_158_2503  Soon after reports of Mr. Mony 's statement st...   \n",
       "3069  train_09_B_159_2947  Ancheri Baby was shot dead on November 13 , 19...   \n",
       "3070   train_09_B_160_479  Mullanchira Mathai was beaten to death on Janu...   \n",
       "3071   train_09_B_161_769  Muttukad Nanappan was stabbed to death on June...   \n",
       "\n",
       "      label         set  sub    eg  \n",
       "0         1    train_01    0  1437  \n",
       "1         0    train_01    1  3212  \n",
       "2         0    train_01    2  3319  \n",
       "3         0    train_01    3  1008  \n",
       "4         0    train_01    4  2655  \n",
       "...     ...         ...  ...   ...  \n",
       "3067      0  train_09_B  157  2377  \n",
       "3068      0  train_09_B  158  2503  \n",
       "3069      0  train_09_B  159  2947  \n",
       "3070      1  train_09_B  160   479  \n",
       "3071      1  train_09_B  161   769  \n",
       "\n",
       "[3072 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "### Run for \"train_subtask1\" and \"dev_subtask1\"\n",
    "main_df = pd.read_csv(\"../data/V2/train_subtask1.csv\", encoding='utf-8-sig')\n",
    "main_df['text'] = main_df['text'].apply(lambda x: x.replace(u'\\ufeff', '').replace(r'[^\\x00-\\x7f]', ''))\n",
    "main_df['set'] = main_df['index'].apply(lambda x: '_'.join(x.split('_')[0:3]) if ('A' in x) or ('B' in x) else '_'.join(x.split('_')[0:2])) #.str.split('_',expand=True)\n",
    "main_df['sub'] = main_df['index'].apply(lambda x: x.split('_')[-2]).astype(int)\n",
    "main_df['eg'] = main_df['index'].apply(lambda x: x.split('_')[-1]).astype(int)\n",
    "main_df = main_df.sort_values(by=['set','sub']).reset_index(drop=True)\n",
    "main_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e0f0a345",
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "data": {
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       "      <td>`` It the protest was something which happened...</td>\n",
       "      <td>0</td>\n",
       "      <td>[O, O, O, B-trigger, O, O, O, O, O, O, O, O, O]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             index                                               text  label  \\\n",
       "0  train_01_0_1437  The State alleged they hacked Sabata Petros Ch...      1   \n",
       "1  train_01_1_3212  Chale was allegedly chased by a group of about...      0   \n",
       "2  train_01_2_3319          His attackers allegedly drank his blood .      0   \n",
       "3  train_01_3_1008  Tip asked Mathunjwa why he had not `` explicit...      0   \n",
       "4  train_01_4_2655  `` It the protest was something which happened...      0   \n",
       "\n",
       "                                              events  \n",
       "0  [O, O, O, O, B-trigger, B-target, I-target, I-...  \n",
       "1  [B-target, O, O, B-trigger, O, O, B-participan...  \n",
       "2  [O, B-participant, O, B-trigger, I-trigger, I-...  \n",
       "3  [O, O, O, O, O, O, O, O, O, O, O, O, O, B-trig...  \n",
       "4    [O, O, O, B-trigger, O, O, O, O, O, O, O, O, O]  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags = []\n",
    "for i,row in main_df.iterrows():\n",
    "    row_text = re.sub(r'[^\\x00-\\x7f]',r'', row.text).strip()\n",
    "    \n",
    "    ### filter to rows with same 'set' and 'subset', e.g. \"train_01\" and \"120\"\n",
    "    t_set = '_'.join(str(row['set']).split('_')[0:2]) # drop \"_A\", \"_B\"\n",
    "    sent_id = int(row['sub'])\n",
    "    if 'B' in str(row['set']):\n",
    "        sent_id+=163 # hard coded, train_09_A + train_09_B== train_09! (in order)\n",
    "    focus_row = df[df['set']=='{}.txt'.format(t_set)].iloc[sent_id]\n",
    "    curr_text = ' '.join(focus_row['text'])\n",
    "    curr_text = re.sub(r'[^\\x00-\\x7f]',r'', curr_text)\n",
    "    curr_tag = focus_row['tags']\n",
    "    ### check if text tallies exactly or not\n",
    "    if row_text == curr_text:\n",
    "        ### if exact match, we take tags as is\n",
    "        tags.append(curr_tag)\n",
    "    elif row_text in curr_text:\n",
    "        ### if NOT exact match, we adjust/remove tokens\n",
    "        _words = []\n",
    "        _tags = []\n",
    "        word_pointer = 0\n",
    "        keep_words = row_text.split(' ')\n",
    "        for j,word in enumerate(curr_text.split(' ')):\n",
    "            if len(_words)==len(keep_words):\n",
    "                break\n",
    "            if word==keep_words[word_pointer]:\n",
    "                _words.append(word)\n",
    "                _tags.append(curr_tag[j])\n",
    "                word_pointer+=1\n",
    "        assert(_words==keep_words)\n",
    "        tags.append(_tags)\n",
    "    else:\n",
    "        ### this should not occur\n",
    "        \n",
    "        raise ValueError(\"Not Implemented!\")\n",
    "        \n",
    "main_df['events'] = tags\n",
    "main_df = main_df[['index','text','label','events']]\n",
    "main_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e67caeb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_df.to_csv(\n",
    "    \"../data/V2/train_subtask1_wevents.csv\", \n",
    "    encoding='utf-8-sig', index=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b77ebecc",
   "metadata": {},
   "source": [
    "### Analyze Event Information x Causal Label\n",
    "Check if there is a correlation between occurence of some events and causal labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0d6bea68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'O',\n",
       " 'etime',\n",
       " 'fname',\n",
       " 'organizer',\n",
       " 'participant',\n",
       " 'place',\n",
       " 'target',\n",
       " 'trigger'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "events_dict = dict(Counter([item for sublist in main_df['events'] for item in sublist]))\n",
    "events = set([i.split('-')[-1] for i in events_dict.keys()])\n",
    "events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8693c1e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'O': 71752,\n",
       " 'B-trigger': 4135,\n",
       " 'B-target': 1309,\n",
       " 'I-target': 1032,\n",
       " 'B-place': 1459,\n",
       " 'I-place': 262,\n",
       " 'B-etime': 1096,\n",
       " 'I-etime': 1437,\n",
       " 'B-fname': 1089,\n",
       " 'I-fname': 2398,\n",
       " 'B-participant': 2354,\n",
       " 'I-trigger': 1273,\n",
       " 'I-participant': 684,\n",
       " 'B-organizer': 1126,\n",
       " 'I-organizer': 1335}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "312cf18f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>O</th>\n",
       "      <th>target</th>\n",
       "      <th>place</th>\n",
       "      <th>etime</th>\n",
       "      <th>organizer</th>\n",
       "      <th>participant</th>\n",
       "      <th>trigger</th>\n",
       "      <th>fname</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3067</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3068</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3069</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3070</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3071</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3072 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      O  target  place  etime  organizer  participant  trigger  fname  total\n",
       "0     0       1      2      1          0            0        1      1      6\n",
       "1     0       1      0      0          0            2        2      0      5\n",
       "2     0       0      0      0          0            1        1      0      2\n",
       "3     0       0      0      0          0            1        2      0      3\n",
       "4     0       0      0      0          0            0        1      0      1\n",
       "...  ..     ...    ...    ...        ...          ...      ...    ...    ...\n",
       "3067  0       0      0      0          0            0        3      0      3\n",
       "3068  0       4      0      1          0            0        1      0      6\n",
       "3069  0       1      0      1          0            0        1      0      3\n",
       "3070  0       1      0      1          1            1        1      0      5\n",
       "3071  0       1      0      1          0            0        1      0      3\n",
       "\n",
       "[3072 rows x 9 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "event_counts = []\n",
    "for tags in main_df['events']:\n",
    "    _event_counts = []\n",
    "    for e in events:\n",
    "        _event_counts.append(tags.count(f'B-{e}'))\n",
    "    event_counts.append(_event_counts)\n",
    "\n",
    "events_df = pd.DataFrame(event_counts, columns=events)\n",
    "events_df['total'] = events_df.sum(axis=1)\n",
    "events_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eb9c7863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "O                  0\n",
       "target          1309\n",
       "place           1459\n",
       "etime           1096\n",
       "organizer       1126\n",
       "participant     2354\n",
       "trigger         4135\n",
       "fname           1089\n",
       "total          12568\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_df.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a9970cff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "820f51e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YDpzfplIlSVR3ZvFXwJ8D/1LXdkpm7gYoXicX7VOBnXX9+oq2l4iIJRHRGxG9/f39LS9akkartodFRFwK7MnMzc1u0qAtG3XMzOWZOScz53R3dw+7RknSi42t4JgXAJdFxCVAFzAxIv4GeDIipmTm7oiYAuwp+vcB0+q27wGeaGvFkjTKtf3MIjNvyMyezJxO7cL1P2bm1cB6YGHRbSFwZ/F+PbAgIk6IiBnATGBTm8uWpFGtijOLg7kJWBsRi4EdwJUAmbk1ItYC24ADwNLMHKiuTEkafSoNi8y8D7iveL8XmHuQfsuAZW0rTJL0It7BLUkqZVhIkkoZFpKkUoaFJKmUYSFJKmVYSJJKGRaSpFKGhSSplGEhSSplWEiSShkWkqRShoUkqZRhIUkqZVhIkkoZFpKkUoaFJKmUYSFJKmVYSJJKGRaSpFKGhSSplGEhSSo1tuoCdPh2fOrMYW/7qk882MJKJI0WhkWFZn/4tmFtd8eEFhciSSUchpIklWp7WETEtIi4NyIeioitEXFd0X5SRNwTEY8UryfWbXNDRGyPiIcj4uJ21yxJo10VZxYHgA9m5uuANwBLI+J04HpgQ2bOBDYUnym+WwDMAuYBN0fEmArqlqRRq+1hkZm7M/OB4v1vgIeAqcB8YGXRbSVwefF+PrAmM5/NzEeB7cD5bS1akka5Sq9ZRMR04Fzge8ApmbkbaoECTC66TQV21m3WV7Q12t+SiOiNiN7+/v4Rq1uSRpvKwiIixgNfBT6Qmb8+VNcGbdmoY2Yuz8w5mTmnu7u7FWVKkqgoLCJiHLWgWJWZtxfNT0bElOL7KcCeor0PmFa3eQ/wRLtqlSRVMxsqgFuBhzLzM3VfrQcWFu8XAnfWtS+IiBMiYgYwE9jUrnolSdXclHcB8E7gwYj4YdH2UeAmYG1ELAZ2AFcCZObWiFgLbKM2k2ppZg60vWpJGsXaHhaZ+W0aX4cAmHuQbZYBy0asKEnSIXkHtySplGEhSSplWEiSShkWkqRShoUkqZRhIUkqZVhIkkoZFpKkUoaFJKmUYSFJKmVYSJJKVbGQoI5ROz515rC3fdUnHmxhJZJazTMLSVIpw0KSVMqwkCSV8pqFXmT2h28b9rZ3TGhhIZI6imcWkqRShoUkqZRhIUkqZVhIkkoZFpKkUoaFJKmUU2d1THMJEqk1PLOQJJUyLCRJpY6aYaiImAd8FhgD3JKZN1VcktrEu8ql6h0VYRERY4C/Bt4M9AHfj4j1mbmt2so0Wh1JgG3+9LtaWInUHkdFWADnA9sz82cAEbEGmA8YFjrqjNRF9yM7A/v0sLctmwgw3LoM1c4SmVl1DaUi4gpgXma+p/j8TuD3MvN9Q/otAZYUH08DHm7B4ScBT7VgP63WiXVZU3OsqXmdWNexXtO/zszuoY1Hy5lFNGh7Scpl5nJgeUsPHNGbmXNauc9W6MS6rKk51tS8TqxrtNZ0tMyG6gOm1X3uAZ6oqBZJGnWOlrD4PjAzImZExPHAAmB9xTVJ0qhxVAxDZeaBiHgf8E1qU2e/nJlb23T4lg5rtVAn1mVNzbGm5nViXaOypqPiArckqVpHyzCUJKlChoUkqZRhcQgRMS8iHo6I7RFxfQfU8+WI2BMRW6quZVBETIuIeyPioYjYGhHXdUBNXRGxKSJ+VNT0yaprGhQRYyLiBxHx9aprGRQRj0XEgxHxw4jorboegIh4ZUSsi4ifFH+3/l3F9ZxW/PkM/vw6Ij5QUS0v+T0QEWdHxP3F/8evRcTElh/XaxaNFUuM/F/qlhgBrqpyiZGIuAjYB9yWmWdUVUe9iJgCTMnMByJiArAZuLziP6cAXpGZ+yJiHPBt4LrM/G5VNQ2KiD8D5gATM/PSquuBWlgAczKzY240i4iVwD9n5i3FDMiXZ+YvKy4LeP53wy5qNwY/XsHxX/J7ICK+D3woM/8pIhYBMzLz4608rmcWB/f8EiOZ+RwwuMRIZTJzI/B0lTUMlZm7M/OB4v1vgIeAqRXXlJm5r/g4rvip/F9FEdED/DFwS9W1dLLiX8UXAbcCZOZznRIUhbnAT6sICjjo74HTgI3F+3uA/9jq4xoWBzcV2Fn3uY+Kfwl2uoiYDpwLfK/iUgaHe34I7AHuyczKawL+Cvhz4F8qrmOoBL4VEZuLJXOq9mqgH/hKMWR3S0S8ouqi6iwAVlddxBBbgMuK91fy4puYW8KwOLimlhhRTUSMB74KfCAzf111PZk5kJnnULvb//yIqHTYLiIuBfZk5uYq6ziICzLzPOCPgKXFMEeVxgLnAV/MzHOB3wKVXzMEKIbELgP+T9W1DLGI2v+7zcAE4LlWH8CwODiXGGlScV3gq8CqzLy96nrqFcMX9wHzqq2EC4DLiusDa4A/iIi/qbakmsx8onjdA9xBbQi2Sn1AX93Z4Dpq4dEJ/gh4IDOfrLqQepn5k8x8S2bOpnbW89NWH8OwODiXGGlCcTH5VuChzPxM1fUARER3RLyyeP8y4A+Bn1RZU2bekJk9mTmd2t+lf8zMq6usCSAiXlFMTKAY6nkLtSGNymTmz4GdEXFa0TSXznkcwVV03hAUETG5eD0O+C/Al1p9jKNiuY8qVLzESEMRsRp4EzApIvqAGzPz1iprovYv5ncCDxbXCAA+mpnfqK4kpgAri1krxwFrM7Njpqp2mFOAO2qZz1jgbzPz7mpLAuBaYFXxD7WfAe+uuB4i4uXUZkf+acV1vOT3ADA+IpYWXW4HvtLy4zp1VpJUxmEoSVIpw0KSVMqwkCSVMiwkSaUMC0lSKcNC6gARcWpErKu6DulgnDorDUNxM2JkZses89SJNenY4ZmFdBAR8WcRsaX4+UBETC+erXAz8AAwLSI+Xjxz4Z6IWB0RHyq2/ZOI+H7xTI2vFjd0ERErIuJzEfGdiPhZRFxRtE8ffD5BsXDe4HMT+iPixqL9w8U+fzz4jI5GNbX/T0qjgWEhNRARs6ndNfx7wBuAPwFOpLYU9G3FAnfd1JaCPhd4G7XnVAy6PTNfn5lnU1u2fXHdd1OAC4FLgZuGHjsz31Msgjgf2AusiIi3ADOprdt0DjC7bsG/52uqatlsHftc7kNq7ELgjsz8LUBE3A78e+DxuocoXQjcmZn/r+jztbrtz4iIvwReCYyntmzMoL8vhoq2RcQpjQ4eEV3UVjZ9X2Y+HhHXUlu36QdFl/HUwmPHkJqkEWFYSI01WqIeastll/UBWEHtiYE/iohrqK3lM+jZJvbxJWpnJ/9Q1++/Zeb/elGRtWeI/BZphDkMJTW2Ebg8Il5erMb6H4B/HtLn28Bbo/bM7/HUnoI3aAKwu1i+/R2Hc+BiQbgJmVk/RPVNYFFxHCJi6uBKo1I7eGYhNVA8U3wFsKlougX4xZA+34+I9cCPgMeBXuBXxdcfp/bEwMeBB6mFR7M+BOyvW8X3S5n5pYh4HXB/sULsPuBqYODw/suk4XHqrHQEImJ8Zu4rZjttBJYMPpNcOpZ4ZiEdmeURcTrQBaw0KHSs8sxCklTKC9ySpFKGhSSplGEhSSplWEiSShkWkqRS/x92SExdFsPnpwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for e in list(events):\n",
    "    sns.countplot(x=events_df[e], hue=main_df['label'])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3ef32972",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jMzOziit0T2FC+nxpTlkAB7RtOGZm1p4KSgoRMbjcgZiZWfsrKClIyntTgYjIP3KamZntkgo9p/DpnMdngGnAKc1VkDRX0gZJy3PKpkl6W1J9+vhCzrIrJL0h6TVJny/6nZiZWasVevjoG7nzkvYBftZCtVuBnwA77k1cFxGzdmhvCDAeOBz4JPC4pIMjwt1fzcwqqNShs/8EHNTcChHxFPCHAts7FbgrIj6IiLeAN4CjSozNzMxKVOg5hQdJehtBMhDeYcD8El/zovQcxRLgkoj4b2AA8GzOOg1pmZmZVVChXVJzD/dsBVZHREMJr3cD8D2SBPM94BrgAkB51o08ZUiaCEwE2H///UsIwczMmlLoOYVfSepHcqIZYGUpLxYR6xunJd0EPJTONgCDclYdCLzTRBtzgDkAdXV1eROHVS+PJWVW3Qq989rZwGLgLOBs4DlJRQ+dLal/zuxpQGPPpAeA8ZK6SxpMcr5icbHtm5lZ6xR6+OhK4NMRsQFAUh/gceCepipIuhM4AegtqQH4F+AESSNIDg2tAv4BICJeljQfeIXk8NQk9zwyM6u8QpPCbo0JIbWJFvYyIuKcPMU3N7P+DGBGgfGYmVkZFJoUfiHpEeDOdP6LwM/LE5KZmbWXlu7R/D+AfhFxqaTTgeNIego9A8yrQHxmZlZBLZ1ovh74I0BELIiIb0XExSR7CdeXNzQzM6u0lpJCbUS8tGNhRCwhuTWnmZl1IC0lhZpmlu3eloGYmVn7aykpPC/pqzsWSroQWFqekMzMrL201PtoCrBQ0pf4KAnUAZ8gufjMzMw6kGaTQjosxWhJY4ChafHDEfHLskdmZmYVV+jYR08AT5Q5FjMza2el3k/BzMw6ICcFMzPLOCmYmVnGScHMzDJOCmZmlnFSMDOzjJOCmZllnBTMzCzjpGBmZplC77xmVjXWTD+iyWX7X7WsgpGYdTzeUzAzs4yTgpmZZZwUzMws46RgZmYZJwUzM8s4KZiZWcZJwczMMmVLCpLmStogaXlOWU9Jj0lamT7vl7PsCklvSHpN0ufLFZeZmTWtnBev3Qr8BLg9p+xyYFFEzJR0eTr/HUlDgPHA4cAngcclHRwR28oYn5XJqEtvb3LZwr0qGIiZFa1sewoR8RTwhx2KTwVuS6dvA8bllN8VER9ExFvAG8BR5YrNzMzyq/Q5hX4RsQ4gfe6blg8A1uas15CWmZlZBVXLiWblKYu8K0oTJS2RtGTjxo1lDsvMrHOpdFJYL6k/QPq8IS1vAAblrDcQeCdfAxExJyLqIqKuT58+ZQ3WzKyzqXRSeACYkE5PAO7PKR8vqbukwcBBwOIKx2Zm1umVrfeRpDuBE4DekhqAfwFmAvMlXQisAc4CiIiXJc0HXgG2ApPc88jMrPLKlhQi4pwmFo1tYv0ZwIxyxWNmZi2rlhPNZmZWBZwUzMws46RgZmYZJwUzM8s4KZiZWcZJwczMMk4KZmaWKefQ2WYl8/DbZu3DewpmZpbxnoJ1SmumH5G3fP+rllU4ErPq4j0FMzPLOCmYmVnGScHMzDJOCmZmlnFSMDOzjJOCmZllnBTMzCzjpGBmZhknBTMzyzgpmJlZxknBzMwyTgpmZpZxUjAzs4yTgpmZZZwUzMws46RgZmYZJwUzM8u0y53XJK0C/ghsA7ZGRJ2knsB/ALXAKuDsiPjv9ojPzKyzas89hTERMSIi6tL5y4FFEXEQsCidNzOzCqqmw0enArel07cB49ovFDOzzqm9kkIAj0paKmliWtYvItYBpM9981WUNFHSEklLNm7cWKFwzcw6h3Y5pwAcGxHvSOoLPCbp1UIrRsQcYA5AXV1dlCtAM7POqF32FCLinfR5A7AQOApYL6k/QPq8oT1iMzPrzCqeFCTtIWmvxmngb4HlwAPAhHS1CcD9lY7NzKyza4/DR/2AhZIaX/+OiPiFpOeB+ZIuBNYAZ7VDbGZmnVrFk0JE/BYYnqd8EzC20vGYlWLN9COaXLb/VcsqGIlZ26qmLqlmZtbOnBTMzCzTXl1Szcpu1KW3N7ls4V4VDMRsF+I9BTMzyzgpmJlZxknBzMwyTgpmZpZxUjAzs4yTgpmZZTpEl9Tmuh4uvfq8CkZiVjhfFW3VyHsKZmaWcVIwM7NMhzh8ZFatfFW17WqcFMya0dSXur/QraPy4SMzM8s4KZiZWcaHj8x2Ye7Wam3NewpmZpbp8HsKTf2S8q8oM7OdeU/BzMwyTgpmZpZxUjAzs0yHP6dgtqtri6uim2/j6rzlPu/WOXlPwczMMk4KZmaWcVIwM7NM1Z1TkHQS8COgC/DTiJjZziGZGU2fl2iLG1n5yuzqUVVJQVIX4P8AfwM0AM9LeiAiXmnfyMystco9jLgvVG0bVZUUgKOANyLitwCS7gJOBZwUzKpUJX/lV0tiaXpI9fw9ufK1UWwMbdFGIfUVEQW9SCVIOhM4KSL+Pp0/Fzg6Ii7KWWciMDGdPQR4rYVmewO/b0VYra3fkdqohhiqpY1qiKFa2qiGGKqljWqIoZA2PhURffItqLY9BeUp+1jWiog5wJyCG5SWRERdyQG1sn5HaqMaYqiWNqohhmppoxpiqJY2qiGG1rZRbb2PGoBBOfMDgXfaKRYzs06n2pLC88BBkgZL+gQwHnignWMyM+s0qurwUURslXQR8AhJl9S5EfFyK5st+FBTmep3pDaqIYZqaaMaYqiWNqohhmppoxpiaFUbVXWi2czM2le1HT4yM7N25KRgZmaZDpsUJJ0k6TVJb0i6vIT6cyVtkLS8FTEMkvSEpBWSXpb0zSLr10haLOnFtP53WxFLF0m/kfRQifVXSVomqV7SkhLb2FfSPZJeTT+Tvy6i7iHpazc+3pM0pYQYLk4/y+WS7pRUU0Ib30zrv1xoDPm2J0k9JT0maWX6vF8JbZyVxrFdUrNdEJuof3X693hJ0kJJ+5bQxvfS+vWSHpX0yWLbyFn2bUkhqXcJcUyT9HbONvKFYmOQ9I30e+NlST8sIYYRkp5t/D+RdFQz9fN+PxT5PvJ+RxS7bX1MRHS4B8lJ6jeBA4BPAC8CQ4ps43hgJLC8FXH0B0am03sBrxcTB8l1G3um092A54BjSozlW8AdwEMl1l8F9G7l3+U24O/T6U8A+7bi7/s7kgtwiqk3AHgL2D2dnw+cX2QbQ4HlQA+SjhqPAweVsj0BPwQuT6cvB35QQhuHkVzE+SRQV0L9vwW6ptM/KDGGvXOmJwM3FttGWj6IpJPJ6pa2tSbimAZ8u8C/Y776Y9K/Z/d0vm8JbTwK/F06/QXgyWbq5/1+KPJ95P2OKHbbyn101D2FbLiMiPgL0DhcRsEi4ingD60JIiLWRcQL6fQfgRUkX0yF1o+I2JLOdksfRfcMkDQQ+J/AT4ut21Yk7U3yT3QzQET8JSLeLbG5scCbEbG6hLpdgd0ldSX5Yi/2OpjDgGcj4k8RsRX4FXBaS5Wa2J5OJUmUpM/jim0jIlZEREtX9TdX/9H0fQA8S3JtULFtvJczuwctbKPN/G9dB1zWUv0W2ihIE/X/EZgZER+k62wooY0A9k6n96GZ7au13w9pvaa+I4ratnJ11KQwAFibM99AkR92W5NUCxxJksmLqddFUj2wAXgsIoqqn7qe5J9tewl1GwXwqKSlSoYaKdYBwEbglvQw1k8l7VFiLOOBO4utFBFvA7OANcA6YHNEPFpkM8uB4yX1ktSD5NfgoBbqNKVfRKxLY1sH9C2xnbZyAfCfpVSUNEPSWuBLwFUl1D8FeDsiXizl9XNclB7KmlvUIZPEwcBnJD0n6VeSPl3C608Brk4/i1nAFYVUyvP9UPD7aOI7ouRtq6MmhRaHy6gkSXsC9wJTdvhV1aKI2BYRI0h+wR0laWiRr30ysCEilhZTL49jI2Ik8HfAJEnHF1m/K8mu9g0RcSTw/0h2a4ui5KLGU4C7S6i7H8kvqMHAJ4E9JH25mDYiYgXJYZbHgF+QHJrc2mylXYCkK0nex7xS6kfElRExKK1/UUvr7/DaPYArKSGZ7OAG4EBgBEnSv6bI+l2B/UgOv1wKzJeU77ukOf8IXJx+FheT7hk3J8/3Q1Hvo7XfETvqqEmhaobLkNSN5A8+LyIWlNpOeqjlSeCkIqseC5wiaRXJYbQTJf17Ca//Tvq8AVhIcoiuGA1AQ86ezj0kSaJYfwe8EBHrS6j7OeCtiNgYER8CC4DRxTYSETdHxMiIOJ7k8MHKEmIBWC+pP0D63OzhinKRNAE4GfhSpAehW+EO4Iwi6xxIkqhfTLfTgcALkv6qmEYiYn36BbkduInSttEF6SGZxSR71s2e8M5jAsl2BckPl2ZjyPf9UOr72OE7ouRtq6MmhaoYLiP9lXEzsCIiri2hfp/G3iCSdif5Unu1mDYi4oqIGBgRtSSfwy8joqhfx5L2kLRX4zTJycmiemVFxO+AtZIOSYvGUtqQ6OdQwqGj1BrgGEk90r/NWJLjuEWR1Dd93h84vRXxPEDyJUL6fH+J7ZRMyU2tvgOcEhF/KrGNg3JmT6H4bXRZRPSNiNp0O20gOQH7uyLj6J8zexpFbqPAfcCJaVsHk3SGKHa00neAz6bTJ9LMD4amvh+KeR/NfEeUvm0VekZ6V3uQHOt9naQX0pUl1L+TZNftQ5KN9MIS2jiO5LDVS0B9+vhCEfWHAb9J6y8HrmrlZ3ICJfQ+Ijkf8GL6eLmUzzNtZwSwJH0/9wH7FVm/B7AJ2KcVn8F303+a5cDPSHuaFNnGf5EktBeBsaVuT0AvYBHJF8cioGcJbZyWTn8ArAceKbL+GyTn3xq3z5Z6DuVr497083wJeBAYUGwbOyxfRcu9j/LF8TNgWRrHA0D/Iut/Avj39L28AJxYQgzHAUvTbeM5YFQz9fN+PxT5PvJ+RxS7beU+PMyFmZllOurhIzMzK4GTgpmZZZwUzMws46RgZmYZJwUzM8s4KVjZpKNdXpMz/21J08rwOnemQwJcvEP5jqNN1quFUUBLeO2pRa4/XdLnili/h6R5SkaoXS7p6fQK2KJJGidpSCl1rfOoqttxWofzAXC6pH+NiGIvAipIetXr6Ij4VBOrXBcRs8rx2qmpwPcLXTkiih3K4ZvA+og4ApIhxEn6xZdiHPAQpV00aJ2E9xSsnLaS3Cv24h0XSPqUpEXpL/xF6dXBTUrHjb8l/cX8G0lj0kWPAn3TvYDPFBJUOuDZ4TnzT0oalV65PVfS8+lrnJouP1/SAkm/UDI+/Q/T8pkkI67Wp7/m95D0sJKx7ZdL+mKe175V0pnp9CpJ35X0Qvq+Ds0Tbn/g7caZiHgt0lE8JX1ZyVj69ZL+r6QuafkWJQPUvahkbP9+kkaTXG18dbr+genjF0oGOfyvxtdPY5wt6deSftsYb7rssjTWF9P3T1Pt2C6q1CtD/fCjpQewhWQY4VUkwwh/G5iWLnsQmJBOXwDc10JblwC3pNOHkgxZUQPU0sQ9L0jGpX+bj64WfSItvxj4bjrdH3g9nf4+8OV0el+SK+L3AM4Hfpu+hxqS8f4HNb7HnNc7A7gpZ36fPDHdCpyZTq8CvpFOfx34aZ71R5CMW/MM8L9J791AMoT3g0C3dP7fgPPS6QD+Vzr9Q+CfdnztdH5RTntHkwyB0rje3SQ/GoeQDEMPybhTvwZ6pPM9m2vHj13z4cNHVlYR8Z6k20luvvLnnEV/TTJuECSX9Td7lyuSIQF+nLb5qqTVJEMdtzTq7HWx8+Gj+SSjnP4LcDYfjbj6tySDB347na8BGvdgFkXEZgBJrwCf4uPDs0MyNMEsST8gGU7kv1qIDT4aPG0pH30emYiol3RAGtvngOeV3LFuLDAqnQfYnY8GPfsLyWGixnb/Zsd20/MSo4G79dFAoN1zVrkvkgHZXpHULy37HEli/lMa2x8KaMd2MU4KVgnXk4wlc0sz67Q03kqxQxg3/UIRb0vaJGkY8EXgH3Je44zY4aY1ko4mOT/SaBt5/nci4nVJo0jGr/lXSY9GxPQWwmlsN2+babtbSJLHAknb0/b/AtwWEfnG6/8wIho/z6ba3Q14N5Ihl5uLCz767MXOf6eW2rFdjM8pWNlFxB9Ifp1fmFP8a5JRWyG5McvTLTTzVLpe4wiW+wMF3XGsCXeR3Hhon4hYlpY9AnxD6U9eSUcW0M6HSoY/Rsm9if8UEf9OcoOVUoYG/xhJxyq9yYqSEX+HkBy+WgScqY9GbO0pqamT7Y3+SHLbRyIZt/8tSWel9SVpeAv1HwUuUHL/AyT1LLEdq2JOClYp1/DxseknA1+R9BJwLkkvGyR9TdLX8tT/N6CLpGXAf5DcW/mDPOvt6GJ9vEtqbVp+D0lSmp+z7vdIbmf4kpKbsX+vgPbnpOvPA44AFiu5C9aVJOcAWutA4Ffp+/4NySiz90bEK8A/kdwN7yWSw2H9m24GSBLhpelJ9ANJkuyFkhpHv232lrUR8QuSUTuXpO+x8TBbUe1YdfMoqWZmlvGegpmZZZwUzMws46RgZmYZJwUzM8s4KZiZWcZJwczMMk4KZmaW+f9I2oBFL3AbkwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x=events_df['total'], hue=main_df['label'])\n",
    "ax.set(xlabel='No. of Events in Sentence', ylabel='Count')\n",
    "plt.legend(labels=['Non-causal', 'Causal'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4b7ca37",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
